How to load data from Fauna to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Fauna data into Databricks Lakehouse within minutes.

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Set up a Fauna connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Fauna data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Fauna to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Export Data from FaunaDB

Begin by exporting your data from FaunaDB. This can be achieved by using FaunaDB's FQL (Fauna Query Language) to query your data and export it to a JSON or CSV format. Utilize FaunaDB's dashboard or Fauna shell to execute a query that retrieves the necessary data and writes it to a file.

Once the data is exported, ensure its integrity and completeness. Open the exported file and verify that all necessary fields and records are present. Check for any anomalies or missing data that might have occurred during the export process.

Log into your Databricks account and set up a new cluster if needed. Ensure that your cluster is properly configured with the necessary compute resources and configurations suited to handle the data you plan to import.

Use the Databricks interface to upload your exported data file to the Databricks File System (DBFS). You can do this by navigating to the 'Data' section in Databricks, selecting 'DBFS', and using the upload functionality to transfer the JSON or CSV file.

Open a new notebook in Databricks and read the uploaded file from DBFS. Use Spark's built-in functions (e.g., `spark.read.json()` or `spark.read.csv()`) to load the file into a DataFrame. Perform any necessary transformations or cleaning operations to prepare the data for integration into the Databricks Lakehouse.

Create a new table in the Databricks Lakehouse to store the imported data. Define the schema of the table to match the structure of your DataFrame. This can be achieved using SQL commands within a Databricks notebook to create a Delta table.

Finally, load the prepared DataFrame into the newly created table. Use Spark's DataFrame API to write data to the Lakehouse. For example, use the `DataFrame.write.format("delta").saveAsTable("tableName")` method to save the DataFrame to the Delta table. Verify that the data has been successfully loaded by querying the table and checking for accuracy.

By following these steps, you can effectively transfer data from FaunaDB to Databricks Lakehouse without relying on third-party connectors or integrations.